5 of the Best Open Source Tools For Big Data

If you’re the owner, CIO, or IT manager of a forward-thinking company, you’re probably searching for ways to capitalize on big data. According to a recent forcasting by IDC, the market is on track to reach $41.5 billion by 2018, which would represent a compound annual growth rate of 26.4 percent. But while organizations are pouring copious amounts of dollars into new technology, tools and services, big data can be a very cost effective investment thanks to the open source movement.

From data storage and management platforms to analytics tools, open source software enables big data to be immensely flexible and even more affordable for companies who may be seeking alternatives to proprietary solutions. Below we examine some of the best of the best.

1. Hadoop

The foundation of most big data implementations, Apache Hadoop is an open source software application designed for large scale distributed computing. Whether it’s a single desktop or thousands of servers, this platform handes large volumes of data with robust processing and storage capabilities at the local level. Hadoop ensures high availability across massive server clusters all without relying on the hardware to do the bulk of the work.

2. MongoDB

MongoDB is one of several NoSQL databases. However, this open source platform claims to be the only of those entries that combines NoSQL innovation with the solid and consistent foundation relational database systems brought to the table. MongoDB is primarily built on three principles: flexibility, scalability, and performance. The system can store and modify any data structure, scale up to thousands of servers, and support heavy duty read-write workloads without buckling.

3. PostgreSQL XL

NoSQL databases have the limitations of their relational predecessors to thank for their recent rise to power. With that said, traditional databases are not going down without a fight, as evidenced in the evolution of contendenders like PostgreSQL XL. The XL has two meanings here as it represents both “eXtensible Lattice” as well as the “extra large” version of the open source database software PostgreSQL. Though not a NoSQL solution, it does support big data projects using traditional SQL.

4. Python

Programming languages are in many ways the unsung heroes of big data projects. These languages play a big role in the process by allowing companies to create scalable custom applications that coordinate operations between databases and huge server clusters. Python is one of the most popular choices in the realm of big data. Open source, flexible, and extremely powerful, Python is relatively easy for analysts to pick up, making it ideal for advanced analytical tasks and managing day-to-day business data.

5. Pentaho

It’s safe to say that there is no big data without analytics, which makes it possible to harness meaningful insights from raw data. Pentaho powers the analytical component by providing access to a collection of open source business intelligence tools with capabilities ranging from data mining to data integration and reporting. Whether it’s the enterprise edition or the free community version, Pentaho benefits from an active community of users, developers, testers, and implementers who help greatly reduce the learning curve.